Dynamic route information interface

ABSTRACT

Various technologies described herein pertain to causing presentation on a user interface of an immediate portion of a navigation route of an autonomous vehicle. A computing system of the autonomous vehicle determines whether an object detected by sensor(s) of the autonomous vehicle proximate to the immediate portion of the navigation route are of a type and relative position defined as one of consequential and inconsequential for a human passenger. In response to determining that an object has both a type and relative position defined as consequential, the computing system causes presentation on the user interface a representation of the object relative to the immediate portion of the navigation route to provide a confidence engendering indication that the autonomous vehicle has detected the object. Otherwise if inconsequential, presentation on the user interface of any representation of the object is not caused by the computing system to avoid creating a confusing presentation.

BACKGROUND

The present application generally relates to autonomously drivenpassenger vehicles and more particularly relates to a user interface ofan autonomously driven passenger vehicle.

Automated controls of vehicles have been utilized for an extended periodin aviation, railroad and ship technology. The respective routes throughair, on rail, and over water tend not to have the close proximity ofother vehicles that complicate path control. More recently, advances inimage recognition combined with range finding and geographical roadnavigation have combined to enable autonomous vehicles to operate onvehicle roadways. The autonomous vehicles have to coexist within closeproximity to other vehicles, pedestrians, cyclists, and road hazards.Object detection and avoidance have received extensive development withmany dynamic predictions and strategies being continuously assessedduring operation of the autonomous vehicles.

Generally-known user interfaces provided in the autonomous vehicle forpassengers include display of the immediate portion of the route of theautonomous vehicle. An upcoming or currently executed path strategy aredisplayed to explain what the autonomous vehicle is doing or will do.However, these simple user interfaces omit significant amounts ofinformation. A passenger can see through the windshield many potentialroad hazards or unpredictable fellow travelers on the roadway. Unless anobject is detected as requiring an immediate countermeasure to avoid,the simple user interface does not provide assurance to a passenger thatthe object is being monitored.

Less well known are engineering-oriented user interfaces that displaydetailed results of object detection, recognition, and predictions aswell as a range of strategies being assessed. Development andcommissioning of an autonomous vehicle by knowledgeable engineersbenefit from being able to monitor a wide range of computation resultsby the autonomous vehicle. However, such a detailed user interface isdisconcerting to a typical passenger who can get lost in the cluttereddisplay. Confidence in the autonomous vehicle can be shaken by seeingobjects displayed that are humanly perceptible as inconsequential.Similarly, seeing a range of strategies being considered can bedisconcerting when a low confidence strategy is displayed that does notappear applicable at all to a passenger.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims. The present innovation is directed at least inpart to presenting to a passenger on a user interface a portion ofobjects detected by an autonomous vehicle and a portion of pathadjustment options being considered by the autonomous vehicle. Expertiseis utilized in filtering possible status information to engenderconfidence in the passenger that the autonomous vehicle is aware ofpotential hazards ahead and has good alternatives to address eachhazard. Filtering also removes inconsequential detected objects and lowconfidence options from the display that could cause confusion ordegrade confidence in the autonomous vehicle.

In accordance with one aspect of the present innovation, a methodincludes causing presentation on a user interface of an immediateportion of a navigation route of an autonomous vehicle. The methodincludes determining whether an object detected by one or more sensorsof the autonomous vehicle proximate to the immediate portion of thenavigation route are of a type and relative position defined as one ofconsequential and inconsequential for a human passenger. In response todetermining that the object has both a type and relative positiondefined as consequential, the method includes causing presentation onthe user interface a representation of the object relative to theimmediate portion of the navigation route to provide a confidenceengendering indication that the autonomous vehicle has detected theobject. In response to determining that the object does not have both atype and relative position defined as consequential and is thusinconsequential, the method includes not causing presentation on theuser interface of any representation of the object to avoid creating aconfusing presentation.

In one aspect of the present disclosure, an autonomous vehicle includesone or more sensors that sense objects in an immediate portion of aroute of the autonomous vehicle. The autonomous vehicle includes a userinterface device that presents a user interface to a passenger of theautonomous vehicle. The autonomous vehicle includes a computing systemthat is in communication with one or more sensors and the user interfacedevice, and that comprises a memory comprising instructions and aprocessor that executes the instructions to cause the autonomous vehicleto perform acts. The acts include causing presentation on the userinterface of the immediate portion of a navigation route of theautonomous vehicle. The acts include determining whether an objectdetected by the one or more sensors of the autonomous vehicle proximateto the immediate portion of the navigation route are of a type andrelative position defined as one of consequential and inconsequentialfor a human passenger. In response to determining that the object hasboth a type and relative position defined as consequential, the actsinclude causing presentation on the user interface a representation ofthe object relative to the immediate portion of the navigation route toprovide a confidence engendering indication that the autonomous vehiclehas detected the object. In response to determining that the object doesnot have both a type and relative position defined as consequential andis thus inconsequential, the acts include not causing presentation onthe user interface of any representation of the object to avoid creatinga confusing presentation.

In one aspect according to the present disclosure, a computer programproduct includes program code on a computer readable storage devicethat, when executed by a processor associated with an electronic device,the program code enables the electronic device to provide functionality.The functionality includes causing presentation on a user interface ofan immediate portion of a navigation route of an autonomous vehicle. Thefunctionality includes determining whether an object detected by one ormore sensors of the autonomous vehicle proximate to the immediateportion of the navigation route are of a type and relative positiondefined as one of consequential and inconsequential for a humanpassenger. In response to determining that the object has both a typeand relative position defined as consequential, the functionalityincludes causing presentation on the user interface a representation ofthe object relative to the immediate portion of the navigation route toprovide a confidence engendering indication that the autonomous vehiclehas detected the object. In response to determining that the object doesnot have both a type and relative position defined as consequential andis thus inconsequential, the functionality includes not causingpresentation on the user interface of any representation of the objectto avoid creating a confusing presentation.

The above summary presents a simplified summary to provide a basicunderstanding of some aspects of the systems and/or methods discussedherein. This summary is not an extensive overview of the systems and/ormethods discussed herein. It is not intended to identify key/criticalelements or to delineate the scope of such systems and/or methods. Itssole purpose is to present some concepts in a simplified form as aprelude to the more detailed description that is presented later.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram that illustrates an exemplaryautonomous vehicle system having a user interface that presents filteredobject monitoring and path adjustment decisions to engender passengerconfidence, according to one or more embodiments;

FIG. 2 is display diagram of an exemplary user interface device of theautonomous vehicle system of FIG. 1, according to one or moreembodiment;

FIG. 3 is a flow diagram illustrating an exemplary methodology forfiltering indications to a passenger of object detection and avoidanceto engender confidence in an autonomous vehicle while avoiding confusingindications, according to one or more embodiment;

FIG. 4A-4B are a flow diagram illustrating an exemplary methodology forpresenting filtered object monitoring and path adjustment decisions toengender passenger confidence, according to one or more embodiments; and

FIG. 5 illustrates an exemplary computing device, according to one ormore embodiments.

DETAILED DESCRIPTION

A method, autonomous vehicle, and computer program product causepresentation on a user interface of an immediate portion of a navigationroute of an autonomous vehicle. A computing system of the autonomousvehicle determines whether an object detected by one or more sensors ofthe autonomous vehicle proximate to the immediate portion of thenavigation route are of a type and relative position defined as one ofconsequential and inconsequential for a human passenger. In response todetermining that an object has both a type and relative position definedas consequential, the computing system causes presentation on the userinterface a representation of the object relative to the immediateportion of the navigation route to provide a confidence engenderingindication that the autonomous vehicle has detected the object.Otherwise if inconsequential, presentation on the user interface of anyrepresentation of the object is not caused by the computing system toavoid creating a confusing presentation.

As set forth herein, like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of one or more aspects. It may be evident,however, that such aspect(s) may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing one or moreaspects. Further, it is to be understood that functionality that isdescribed as being carried out by certain system components may beperformed by multiple components. Similarly, for instance, a componentmay be configured to perform functionality that is described as beingcarried out by multiple components.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Further, as used herein, the terms “component” and “system” are intendedto encompass computer-readable data storage that is configured withcomputer-executable instructions that cause certain functionality to beperformed when executed by a processor. The computer-executableinstructions may include a routine, a function, or the like. It is alsoto be understood that a component or system may be localized on a singledevice or distributed across several devices. Further, as used herein,the term “exemplary” is intended to mean serving as an illustration orexample of something and is not intended to indicate a preference.

With reference now to FIG. 1, illustrated is an exemplary autonomousvehicle 100 that can navigate about roadways without human conductionbased upon sensor signals output by sensor systems. The autonomousvehicle 100 includes a plurality of sensor systems 104 a-n (a firstsensor system 104 a through an Nth sensor system 104 n). The sensorsystems 104 a-n are of different types and are arranged about theautonomous vehicle 100. For example, the first sensor system 104 a maybe a lidar sensor system and the Nth sensor system 104 n may be a camera(image) system. Other exemplary sensor systems include radar sensorsystems, GPS sensor systems, sonar sensor systems, infrared sensorsystems, and the like.

The autonomous vehicle 100 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle100. For instance, the mechanical systems can include but are notlimited to, a vehicle propulsion system 106, a braking system 108, and asteering system 110. The vehicle propulsion system 106 may be anelectric motor, an internal combustion engine, or a combination thereof.The braking system 108 can include an engine break, brake pads,actuators, and/or any other suitable componentry that is configured toassist in decelerating the autonomous vehicle 100. The steering system110 includes suitable componentry that is configured to control thedirection of movement of the autonomous vehicle 100.

The autonomous vehicle 100 additionally comprises a computing system 112that is in communication with the sensor systems 104 a-n and is furtherin communication with the vehicle propulsion system 106, the brakingsystem 108, and the steering system 110. The computing system 112includes a processor 114 and memory 116 that includescomputer-executable instructions that are executed by the processor 114.In an example, the processor 114 can be or include a graphics processingunit (GPU), a plurality of GPUs, a central processing unit (CPU), aplurality of CPUs, an application-specific integrated circuit (ASIC), amicrocontroller, a programmable logic controller (PLC), a fieldprogrammable gate array (FPGA), or the like.

The memory 116 comprises an object recognition system 118 that isconfigured to assign labels to objects (in proximity to the autonomousvehicle 100) captured in sensor signals output by the sensor systems 104a-104 n. The memory 116 additionally includes a control system 120 thatis configured to receive output of the object recognition system 118 toadjust a route and is further configured to control at least one of themechanical systems (the vehicle propulsion system 106, the brake system108, and/or the steering system 110) based upon the output of the objectrecognition system 118.

The route for the autonomous vehicle 100 can be locally determined. Inone or more embodiments, a communication module 122 encodes and decodescommunication protocols for a transceiver 124 to transceive via anetwork 126 to an administration system 128. Administration system 128can perform routing for a population of autonomous vehicles 100. Pathadjustments to the route can be made locally at the autonomous vehicle100 by a path adjustment system 130 executed by computing system 112 andresponsive to sensor systems 104 a-n. In real-time, computing system 112evaluates numerous objects with object recognition system 118 andconsiders numerous path changes with path adjustment system 130. Aportion of the objects that are recognized and monitored are presentedto a passenger 132 via a user interface (UI) device 134. Additionally oralternatively, one or more representations of the path changes evaluatedby the path adjustment system 130 can be presented to the passenger 132via the user interface device 134.

Since the passenger 132 can see potential road hazards approaching theautonomous vehicle 100, user interface device 134 presents filteredobject monitoring results 136 and filtered path decisions 138 thatprovide situational awareness without being confusing or disheartening.To that end, a UI filtration expert system 140 executed by computingsystem 112 provides categorizations of recognized objects and pathadjustments being considered. UI filtration expert system 140 filtersaccording to those categorization to cause presentation of informationthat passenger 132 would consider as consequential and not what would beinconsequential. Passenger 132 would tend to readily recognize manyobjects proximate to the path of autonomous vehicle 100 asinconsequential, presenting no hazard. For example, if remaining in apresent road lane, the autonomous vehicle would not identify fixedobjects such as a fire hydrant, telephone pole, or building to behazardous. Conversely, a child or pet on the sidewalk next to a roadwaycould unexpectedly move onto the lane in front of the autonomous vehicle100. Cyclists or vehicles in other lanes could ill-advisedly swerve intoa path of the autonomous vehicle. Passenger 130 would benefit fromknowing that such consequential objects are recognized and being trackedby computing system 112. Similarly, objects that prompt a pathadjustment of the autonomous vehicle 100 warrant planning for possiblepath adjustments. Some strategies can be unwarranted or ineffective andreceive a corresponding low confidence level by computing system 112.Filtering such unlikely strategies from passenger 132 can be beneficialin avoiding alarm that the autonomous vehicle 100 is even consideringsuch strategies.

TABLE 1 is an example of an expert rule-based system for categorizingobjects as being either consequential or inconsequential, assuming beingwithin a proximate distance from a path of an autonomous vehicle.

TABLE 1 Size Template Motion Position Status Strategy Small No Motion Inpath Consequential Includes straddling if lane object or change smallerunavailable to prevent tire damage. Indication given of road debris.Abrupt strategy discouraged. Any size No In path ConsequentialStraddling not an larger than Motion option. Abrupt small strategydiscouraged. object Larger No Not in path Inconsequential May be abuilding or than a Motion tree. Provide no vehicle indication. SmallerNo Not in path Inconsequential Items like fire than child Motionhydrants. Provide no indication. Human Motion Not in path Consequentialif Predict possible sized or No within range of movements that Motionmovement could intersect with path. Cyclist Motion Not in path butConsequential if Predict possible sized or No on roadway within range ofmovements Motion movement according to roadway and traffic control(including small chance of human error) that could intersect with path.Any size Motion Not in path yet Consequential Predict possible towardbut predicted to movements and path have an implement avoidanceintersecting strategy. path Any size Motion Predicted to ConsequentialIndicate object toward cross path recognition but no path within 3 caravoidance strategy. lengths but paths not predicted to intersect Anysize Motion Predicted to Inconsequential Filter indication. toward crosspath more path than 3 car lengths away

In one or more embodiments, a rule-based expert system can be generatedby training a neural net to mimic filtering for consequential andinconsequential objects based on human decision making. Video or stillimage scenarios can be presented to a human test subjects to elicitwhether any objects detected would be deemed consequential for display.In one or more embodiments, image recognition capabilities aresufficient to label objects into tightly defined categories such aschild, pet, ball, bicyclist, motor cyclist, van, truck, car, tree,building, street sign, traffic light, road debris, etc. Each type ofobject can have a classification assigned.

FIG. 2 is graphical screen depiction illustrating a user interface 200that includes a textual route summary 202 and a perspective first personview of an immediate portion of a route 204. The route is based ongeographical map and current traffic conditions that is stored on theautonomous vehicle or received from a remote source. A depiction of anautonomous vehicle 206 is localized within the immediate portions withany path deviation or adjustment 208 annotated on the route 204. Certaingeographic fixed features 210 such as a side road can be depicted toenhance situational awareness of a passenger, enabling matching of theuser interface 200 to what is humanly viewable through windshield. Inone or more embodiments, user interface 200 can be an augmented realityview superimposed upon a heads-up display through which the actual frontwindshield scene is viewed or on a display that merges a video imagewith augmentation.

A traffic control device indication 212, such as a traffic light,corresponds to the status of the next traffic control device along theroute. Object recognition can be relied upon to ascertain currenttraffic signals. In one or more embodiments, positioning of such trafficcontrols is known in advance and/or status information is communicatedto the autonomous vehicle. Display of any applicable traffic controlshelp to explain instances where the autonomous vehicle is stopping inresponse to a traffic control. Traffic signage that is informationalrather than route affecting can be omitted from display to avoidcluttering the user interface 200.

Objects can be displayed that are in the roadway, such as parkedvehicles 214 a-214 b and a stopped or slowing vehicle 214 c that is inthe same lane 215 as the autonomous vehicle 206. Stopped or slowingvehicle 214 c is deemed a consequential object warranting display aswell as prompting development of strategies 216 a-216 b respectively forfinding a path with 80% confidence and slow or stop within the same lanewith 25% confidence. Presentation of the backup strategy can engenderconfidence that the autonomous vehicle is contemplating alternatives incase the primary strategy becomes unviable due to further situationalchanges. However, low confidence strategies that are unlikely to bechosen nor unlikely to succeed if chosen are not displayed. Strategies216 a-216 b can be path deviation or adjustment 208 that does not affectthe route, such as represented by a next turn indication 218. Pathdeviation or adjustment 208 can represent a lane change into a left-handlane 219, for instance.

Object indication 220 along a left side of user interface 200 representsan object that is image recognized or has a size and position that isdeemed consequential for displaying. Objects in this proximity ofcertain sizes or types can create anxiety in a passenger unless thepassenger understands the autonomous vehicle is aware of the object.Predictions as to possible future vectors 222 for the object indication220 can be displayed to give an indication that the planned path of theautonomous vehicle 206 will avoid the object indication 220. Forexample, a current vector can be a stationary position. Based on imagerecognition, the object can be deemed to be mobile and capable of movingwithin certain ranges of speeds. Given past movement, the type ofobject, aspects of the roadway and traffic signals, predicted vectorscan have evaluated probabilities. For example, a cyclist can be expectedto obey certain rules of the roads. Conversely, a small human-likeobject can conservatively be expected to act erratically like a smallchild or dog. If the object is already moving, a high probability can beassessed that the vector will remain unchanged, at least in the shortterm.

As set forth herein, the term “sized” is intended to mean prediction.For instance, cyclist sized is intended to mean that an object ispredicted to be a cyclist. Accordingly, size is one aspect utilized topredict whether an object is a cyclist. Moreover, it is to beappreciated that sometimes there are objects that are human sized thatare not actually human (e.g., a standalone mailbox); thus, aspects otherthan size can be utilized to predict whether an object having a size ofa human is actually a human.

Stationary object 226 along a right side of the user interface 200represents a small object such as a fire hydrant that is detected butnot displayed because the object is deemed inconsequential. Stationaryobject 228 farther away on the left side represents a large object thatis not in the path of the autonomous vehicle 206, and is thus deemed tobe inconsequential. Distant, moving object 230 represents an object thatis far enough away and detected as leaving the path well in advance ofthe autonomous vehicle 206, and is thus deemed to be inconsequential.

FIG. 3 is a flow diagram illustrating an exemplary methodology or method300 for filtering indications to a passenger of object detection andavoidance to engender confidence in an autonomous vehicle while avoidingconfusing indications. While the methodology is shown and described asbeing a series of acts that are performed in a sequence, it is to beunderstood and appreciated that the methodology is not limited by theorder of the sequence. For example, some acts can occur in a differentorder than what is described herein. In addition, an act can occurconcurrently with another act. Further, in some instances, not all actsmay be required to implement a methodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

Method 300 begins detecting objects proximate to a path of an autonomousvehicle with one or more sensors of the autonomous vehicle (block 302).Method 300 includes characterizing, by a computing system of theautonomous vehicle, one or more attributes of: (i) size; (ii) shape;(iii) position; and (iv) trajectory of the objects to predict alikelihood of intersecting with a path of the autonomous vehicle (block304). In one or more embodiments, method 300 includes performing objectrecognition of each object to determine a most likely label for eachobject with an associated confidence value (block 306). Based upon thelabel, position and trajectory, method 300 includes determiningprobabilities of one or more projected movements by each object (block308). A determination is made whether the probability of at least oneprojected movement by a particular object intersecting with the path ofthe autonomous vehicle is greater than a threshold probability (decisionblock 310). In response to determining that the probability of at leastone projected movement by a particular object intersecting with the pathof the autonomous vehicle is not greater than the threshold probability,method 300 returns to block 302 to continue monitoring for objects thatwarrant indication or a path adjustment strategy. In response todetermining that the probability of at least one projected movement by aparticular object intersecting with the path of the autonomous vehicleis greater than the threshold probability, method 300 includesdetermining one or more strategies to adjust the path to avoidintersecting with the particular object, each strategy having anevaluated confidence level (block 312). Method 300 includes filteringcandidate indications to exclude any particular objects having one of:(i) a label associated with a predetermined inconsequential nature; and(ii) a probability of intersecting with the path of autonomous vehiclebeing below a display threshold (block 314). Method 300 includesfiltering candidate indications of any strategies to adjust the paththat are below a confidence threshold (block 316). Method 300 includespresenting, via a user interface device to a passenger, one or morefiltered indications that the autonomous vehicle has one of: (i)detected the particular object; and (ii) evaluated one or more pathadjustment strategies to avoid the particular object (block 318). Thenmethod returns to block 302 to continue monitoring for objects.

FIGS. 4A-4B illustrate an exemplary method 400 of filtering detectedobjects and avoidance strategies for presenting to a passenger of anautonomous vehicle. In one or more embodiments, method 400 includescausing, by a computing system, presentation on a user interface of animmediate portion of a navigation route of the autonomous vehicle (block402). Method 400 includes determining a type and relative position ofany object detected by one or more sensors of the autonomous vehicleproximate to the immediate portion of the navigation route (block 404).Method 400 includes causing the user interface to present an indicationof an object that is recognized as a traffic control device that affectsthe immediate portion of the route of the autonomous vehicle (block406). Method 400 includes accessing a rule-based system that mapscombinations of size, motion, and position to one of consequential andinconsequential to a human passenger (block 408). A determination ismade as to whether any object is consequential (decision block 410). Inresponse to determining that the object does not have both a type andrelative position defined as consequential and is thus inconsequential,method 400 includes not causing presentation on the user interface ofany representation of the object to avoid creating a confusingpresentation (block 412). Then method returns to block 402 to continuemonitoring.

In response to determining that the object has both a type and relativeposition defined as consequential in decision block 410, method 400includes causing presentation on the user interface a representation ofthe object relative to the immediate portion of the navigation route toprovide a confidence engendering indication that the autonomous vehiclehas detected the object (block 414). Method 400 includes detecting andupdating a vector of position and possible motion of an object in theimmediate portion of the route (block 416). Method 400 includespredicting the probability that the detected vector of the object willintersect with a path of the autonomous vehicle (block 418). Adetermination is made whether the probability that the detected vectorof the object will intersect the path is greater than a intersectionthreshold probability (decision block 420). In response to not detectinga possible intersection, method 400 returns to block 402 to continuemonitoring route and objects. In response to detecting a possibleintersection in decision block 420, method 400 includes generating orreceiving one or more strategies to adjust a path of the immediateportion of the route to avoid the possible intersection (block 422).Method 400 includes prioritizing the one or more strategies based on aconfidence level associated with each strategy (block 424). Method 400includes causing the user interface to present one or more strategiesthat are limited to two having an associated confidence level above aconfidence threshold to further engender confidence in a passenger thatat least one strategy has been evaluated with high confidence to avoidthe object without presenting any confusing low confidence strategies(block 426). Method 400 includes causing the user interface to presentan indication of the generated strategy (block 428). Method 400 includesdirecting the autonomous vehicle to perform the strategy with thehighest confidence (block 430). Then method 400 returns to block 402 tocontinue monitoring.

Referring now to FIG. 5, a high-level illustration of an exemplarycomputing device 500 that can be used in accordance with the systems andmethodologies disclosed herein is illustrated. For instance, thecomputing device 500 may be or include the computing system 112 of theautonomous vehicle 100. According to another example, the administrationsystem 128 of FIG. 1 can be or include the computing device 500. Thecomputing device 500 includes at least one processor 502 (e.g., theprocessor 114) that executes instructions that are stored in a memory504 (e.g., the memory 116). The instructions may be, for instance,instructions for implementing functionality described as being carriedout by one or more systems discussed above or instructions forimplementing one or more of the methods described above. The processor502 may be a GPU, a plurality of GPUs, a CPU, a plurality of CPUs, amulti-core processor, etc. The processor 502 may access the memory 504by way of a system bus 506.

The computing device 500 additionally includes a data store 508 that isaccessible by the processor 502 by way of the system bus 506. Thecomputing device 500 also includes an input interface 510 that allowsexternal devices to communicate with the computing device 500. Forinstance, the input interface 510 may be used to receive instructionsfrom an external computer device, etc. The computing device 500 alsoincludes an output interface 512 that interfaces the computing device500 with one or more external devices. For example, the computing device500 may transmit control signals to the vehicle propulsion system 106,the braking system 108, and/or the steering system 110 by way of theoutput interface 512.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 500 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 500.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and Blu-ray disc (BD), where disks usually reproducedata magnetically and discs usually reproduce data optically withlasers. Further, a propagated signal is not included within the scope ofcomputer-readable storage media. Computer-readable media also includescommunication media including any medium that facilitates transfer of acomputer program from one place to another. A connection, for instance,can be a communication medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio and microwave are includedin the definition of communication medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims. Furthermore, to the extent that theterm “includes” is used in either the detailed description or theclaims, such term is intended to be inclusive in a manner similar to theterm “comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. A method comprising: causing presentation on auser interface of an immediate portion of a navigation route of anautonomous vehicle; determining whether an object detected by one ormore sensors of the autonomous vehicle proximate to the immediateportion of the navigation route are of a type and relative positiondefined as one of consequential or inconsequential for a humanpassenger; in response to determining that the object has both a typeand relative position defined as consequential, causing presentation onthe user interface a representation of the object relative to theimmediate portion of the navigation route to provide a confidenceengendering indication that the autonomous vehicle has detected theobject; in response to determining that the object does not have both atype and relative position defined as consequential and is thusinconsequential, not causing presentation on the user interface of anyrepresentation of the object to avoid creating a confusing presentation;and in response to a path in the immediate portion of the navigationroute of the autonomous vehicle being adjusted based on the object,causing the user interface to present an indication of the path asadjusted that depicts avoidance of the representation of the object. 2.The method of claim 1, further comprising: detecting a vector thatspecifies a predicted change in position of the object in the immediateportion of the navigation route; predicting whether the detected vectorof the object will intersect with the path in the immediate portion ofthe navigation route of the autonomous vehicle; and in response todetecting a possible intersection: generating a strategy to adjust thepath in the immediate portion of the navigation route to avoid thepossible intersection; and causing the user interface to present anindication of the generated strategy, wherein the indication of thegenerated strategy and the indication of the path as adjusted are bothpresented as part of the user interface.
 3. The method of claim 1,further comprising: receiving two or more strategies for adjusting thepath in the immediate portion of the navigation route to avoid theobject; prioritizing the two or more strategies based on a confidencelevel associated with each strategy; and causing the user interface topresent indications of one or more strategies having an associatedconfidence level above a confidence threshold to further engenderconfidence that at least one strategy has been evaluated with highconfidence to avoid the object without presenting any confusing lowconfidence strategies.
 4. The method of claim 3, wherein causing theuser interface to present the indications of the one or more strategiescomprises limiting a number of presented strategies to two.
 5. Themethod of claim 3, further comprising directing the autonomous vehicleto perform the strategy with a highest confidence.
 6. The method ofclaim 1, wherein determining whether the object is of a type andrelative position defined as one of consequential or inconsequential fora human passenger comprises accessing a rule-based system that mapscombinations of size, motion, and position to one of consequential orinconsequential.
 7. The method of claim 1, further comprising causingthe user interface to present an indication of an object that isrecognized as a traffic control device that affects the immediateportion of the navigation route of the autonomous vehicle.
 8. Anautonomous vehicle comprising: one or more sensors that sense objects inan immediate portion of a navigation route of the autonomous vehicle; auser interface device that presents a user interface to a passenger ofthe autonomous vehicle; and a computing system that is in communicationwith one or more sensors and the user interface device and thatcomprises a memory comprising instructions and a processor that executesthe instructions to cause the autonomous vehicle to perform actscomprising: causing presentation on the user interface of the immediateportion of the navigation route of the autonomous vehicle; determiningwhether an object detected by the one or more sensors of the autonomousvehicle proximate to the immediate portion of the navigation route areof a type and relative position defined as one of consequential orinconsequential for a human passenger; in response to determining thatthe object has both a type and relative position defined asconsequential, causing presentation on the user interface arepresentation of the object relative to the immediate portion of thenavigation route to provide a confidence engendering indication that theautonomous vehicle has detected the object; in response to determiningthat the object does not have both a type and relative position definedas consequential and is thus inconsequential, not causing presentationon the user interface of any representation of the object to avoidcreating a confusing presentation; and in response to a path in theimmediate portion of the navigation route of the autonomous vehiclebeing adjusted based on the object, causing the user interface topresent an indication of the path as adjusted that depicts avoidance ofthe representation of the object.
 9. The autonomous vehicle of claim 8,further comprising: detecting a vector that specifies a predicatedchange in position of the object in the immediate portion of thenavigation route; predicting whether the detected vector of the objectwill intersect with the path in the immediate portion of the navigationroute of the autonomous vehicle; and in response to detecting a possibleintersection: generating a strategy to adjust the path in the immediateportion of the navigation route to avoid the possible intersection; andcausing the user interface to present the generated strategy, whereinthe generated strategy and the indication of the path as adjusted areboth presented as part of the user interface.
 10. The autonomous vehicleof claim 8, wherein the processor executes the instructions to cause theautonomous vehicle to perform acts comprising: receiving two or morestrategies for adjusting the path in the immediate portion of thenavigation route to avoid the object; prioritizing the two or morestrategies based on a confidence level associated with each strategy;and causing the user interface to present one or more strategies havingan associated confidence level above a confidence threshold to furtherengender confidence that at least one strategy has been evaluated withhigh confidence to avoid the object without presenting any confusing lowconfidence strategies.
 11. The autonomous vehicle of claim 10, whereincausing the user interface to present the one or more strategiescomprises limiting a number of presented strategies to two.
 12. Theautonomous vehicle of claim 10, wherein the processor executes theinstructions to cause the autonomous vehicle to perform acts comprisingdirecting the autonomous vehicle to perform the strategy with a highestconfidence.
 13. The autonomous vehicle of claim 8, wherein determiningwhether the object is of a type and relative position defined as one ofconsequential or inconsequential for a human passenger comprisesaccessing a rule-based system that maps combinations of size, motion,and position to one of consequential or inconsequential.
 14. Theautonomous vehicle of claim 8, wherein the processor executes theinstructions to cause the autonomous vehicle to perform acts comprisingcausing the user interface to present an indication of an object that isrecognized as a traffic control device that affects the immediateportion of the navigation route of the autonomous vehicle.
 15. Acomputer program product comprising: a computer readable storage device;and program code on the computer readable storage device that whenexecuted by a processor associated with an electronic device, theprogram code enables the electronic device to provide the functionalityof: causing presentation on a user interface of an immediate portion ofa navigation route of an autonomous vehicle; determining whether anobject detected by one or more sensors of the autonomous vehicleproximate to the immediate portion of the navigation route are of a typeand relative position defined as one of consequential or inconsequentialfor a human passenger; in response to determining that the object hasboth a type and relative position defined as consequential, causingpresentation on the user interface a representation of the objectrelative to the immediate portion of the navigation route to provide aconfidence engendering indication that the autonomous vehicle hasdetected the object; in response to determining that the object does nothave both a type and relative position defined as consequential and isthus inconsequential, not causing presentation on the user interface ofany representation of the object to avoid creating a confusingpresentation; and in response to a path in the immediate portion of thenavigation route of the autonomous vehicle being adjusted based on theobject, causing the user interface to present an indication of the pathas adjusted that depicts avoidance of the representation of the object.16. The computer program product of claim 15, wherein the program codeenables the electronic device to provide the functionality of: detectinga vector that specifies a predicted change in position of the object inthe immediate portion of the navigation route; predicting whether thedetected vector of the object will intersect with the path in theimmediate portion of the navigation route of the autonomous vehicle; andin response to detecting a possible intersection: generating a strategyto adjust the path in the immediate portion of the navigation route toavoid the possible intersection; and causing the user interface topresent the generated strategy, wherein the generated strategy and theindication of the path as adjusted are both presented as part of theuser interface.
 17. The computer program product of claim 15, whereinthe program code enables the electronic device to provide thefunctionality of: receiving two or more strategies for adjusting thepath in the immediate portion of the navigation route to avoid theobject; prioritizing the two or more strategies based on a confidencelevel associated with each strategy; and causing the user interface topresent one or more strategies having an associated confidence levelabove a confidence threshold to further engender confidence that atleast one strategy has been evaluated with high confidence to avoid theobject without presenting any confusing low confidence strategies. 18.The computer program product of claim 17, wherein causing the userinterface to present the one or more strategies comprises limiting anumber of presented strategies to two.
 19. The computer program productof claim 16, wherein the program code enables the electronic device toprovide the functionality of directing the autonomous vehicle to performthe strategy with a highest confidence.
 20. The computer program productof claim 15, wherein the program code enables the electronic device toprovide the functionality of determining whether the object is of a typeand relative position defined as one of consequential or inconsequentialfor a human passenger by accessing a rule-based system that mapscombinations of size, motion, and position to one of consequential orinconsequential.